Factors Associated with Household Food Security in Zambia

sustainability

Article
Factors Associated with Household Food Security
in Zambia
William Nkomoki 1 , Miroslava Bavorová 2 and Jan Banout 1, *
 1 Department of Sustainable Technology, Faculty of Tropical AgriSciences, Czech University of Life Sciences
 Prague, Kamýcká 129, 165 00 Prague 6-Suchdol, Czech Republic; nkomoki@ftz.czu.cz
 2 Department of Economics and Development, Faculty of Tropical AgriSciences, Czech University of Life
 Sciences Prague, Kamýcká 129, 165 00 Prague 6-Suchdol, Czech Republic; bavorova@ftz.czu.cz
 * Correspondence: banout@ftz.czu.cz; Tel.: +420-224-384-186; Fax: +420-23438-1829
 
 Received: 9 April 2019; Accepted: 7 May 2019; Published: 13 May 2019 


 Abstract: Food security is a global challenge and threatens mainly smallholder farmers in developing
 countries. The main aim of this paper is to determine factors that are associated with food security in
 Zambia. This study utilizes the household questionnaire survey dataset of 400 smallholder farmers
 in four districts conducted in southern Zambia in 2016. To measure food security, the study employs
 two food security indicators, namely the food consumption score (FCS) and the household hunger
 scale (HHS). Two ordered probit models are estimated with the dependent variables FCS and HHS.
 Both the FCS and HHS models’ findings reveal that higher education levels of household head,
 increasing livestock income, secure land tenure, increasing land size, and group membership increase
 the probability of household food and nutrition security. The results imply that policies supporting
 livestock development programs such as training of farmers in animal husbandry, as well as policies
 increasing land tenure security and empowerment of farmers groups, have the potential to enhance
 household food and nutrition security.

 Keywords: determinants; food security; hunger; policies; smallholder




1. Introduction
 Food insecurity and undernourishment are on the rise worldwide, from an estimated 777 million
people in 2015 to 815 million people in 2016 [1]. This increase is a global concern in achieving the
second sustainable development goal, which calls for a commitment to end hunger, reduce food
insecurity, and improve nutrition by 2030 [1]. The majority of food-insecure populations reside in
Africa, which is home to the largest number of the poorest and most poverty-stricken countries in the
world [2]. Zambia is not spared, as the global hunger index report (GHI) ranks Zambia under the
category of alarming levels of hunger [3]. In Zambia, the predominant livelihood activity is smallholder
farming, mainly cultivating maize and livestock raring. The country’s main labor force is agriculture,
which employs 72% of the national population. Furthermore, the smallholder farmers are adversely
affected by food insecurity.
 Previous studies that dealt with determinants of food security considered the following:
(i) household head characteristics comprising gender, age, education, farming experience, and marital
status; (ii) household characteristics constituting of incomes, livestock ownership, and employment
status; (iii) farm characteristics including land size and land ownership; and (iv) institutional
characteristics, including access to credit, farmers groups, and extension services, which are then
detailed in the conceptual link on determinants of food security. The current study builds on and
extends the study by Nkomoki et al. [4] that determine factors that influences the adoption probability
of sustainable agriculture practices (SAPs), considering the effect of land tenure, and test the association


Sustainability 2019, 11, 2715; doi:10.3390/su11092715 www.mdpi.com/journal/sustainability
Sustainability 2019, 11, 2715 2 of 18



between SAPs use and food security in Zambia. The findings of the study reveal that land tenure
contributed to adoption of SAPs and the chi-square tests indicated that adoption of SAPs contributed
to food security status. However, we acknowledge that results in distribution of food security scores
can not only be attributed to land tenure alone as other factors can play an important role. Therefore,
to further understand households’ food security drivers, we follow up with the ordered probit
regression model analysis.
 The literature investigating the determinants of food security in Zambia is limited. To the best of
the researchers’ knowledge, there is scarcity in the literature on this topic; yet, it is a critical subject in
Zambia. Therefore, this study aims to consolidate past studies to add an incremental contribution with
focus on examining the effect of chosen factors as influencers on food security. The significance of the
study is to provide information to policy-makers, so that they can gain an understanding of a range of
factors that potentially promote food security.
 The structure of the paper is as follows: the next section covers a review of the literature relating
to conceptual links on factors affecting food security. In the third section, the study area, data collection,
and methods of data analysis are described, followed by the results and discussion. The paper ends
with conclusions.

2. Conceptual Link on Determinants of Food Security
 Following prior literature, several factors are associated and considered as determinants of food
security. Holden and Ghebru [5] contended that, for smallholder farmers, the ultimate goal is to
achieve food security.
 There are a number of studies that investigated the effect of gender of household head on food
security. Mallick and Rafi [6] examined the food security status of male- and female-headed households
in Bangladesh. Their results revealed that gender of household head had no effect on household security
and this was attributed to no cultural and social restriction for women’s participation in labor force.
A study by Kassie et al. [7] assessed how gender of household heads was associated with food security
in Kenya. They documented that female-headed households were more vulnerable to food insecurity
than male-headed households. Similarly, Tibesigwa and Visser [8] evaluated the impacts of gender
inequality among smallholder households in South Africa on food security. Their results revealed
that male-headed households were more food-secure when compared to their female counterparts.
Further, they indicated that a wider gap in food security was observed in rural areas in contrast to the
households in urban areas [8].
 De Cock et al. [9] investigated the household food security situation in rural South Africa.
The multivariate analyses indicated that education of household head positively contributed to
food security. Maitra and Rao [10] examined the factors affecting household food security in
Kolkata, India. The findings of the ordered probit model revealed that a household head with
higher education level increased the chance of household being food-secure. Using the logistic
regression model, Zhou et al. [11] explored the factors that influence food security in rural Pakistan.
The results demonstrated that education of the household level played an important contribution
toward households being food-secure.
 A study by De Cock et al. [9] investigated the determinants of food security in rural South Africa,
and the multivariate regression analyses found that household size was a major determinant of
household food security, and a smaller household size was less likely to be food-insecure. A study
by Kabunga et al. [12] used the Household Food Insecurity Access Scale to measure household food
security, and found that larger household sizes are associated with higher food insecurity in Kenya.
In contrast, the findings of the study by Maitra and Rao [10] in India indicated that a larger household
size had less likelihood to be found in a food-insecure category. The contention is that, with a larger
household, the number of bread-winners that the household may depend on for household provision
is higher.
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 In many developing countries, non-farm income is viewed as an opportunity to broaden income
base and contribute to food security. However, Owusu et al. [13] indicated the limited number of studies
linking the relationship between non-farm income with food security. Babatunde and Qaim [14] focused
on the effects of off-farm incomes on food and nutritional security in Nigeria. The results demonstrated
that off-farm income positively affected food security and nutrition. Owusu et al. [13] studied the
impact of off-farm works on household food security in northern Ghana and found that it positively
contributed to household food security. Also, in northern Ghana, Zereyesus et al. [15] evaluated the
influence of participating in non-farm income activities on food poverty. They found that involvement
in non-agriculture activities improved households’ food consumption.
 Generoso [16] studied the impacts of remittances on food security in rural Mali. Results of the
logistic regression model indicted that households with remittances had better food security status
than those without remittances in the Saharan zone, but contended that the benefit to solve food
insecurity was temporary. However, in the same study, no statistical association and contribution
was observed for remittances on food security in the Sahelian zone, Mali. A study by Fransen and
Mazzucato [17] focused on remittances and household wealth for post-conflict households in Burundi.
The findings revealed that, for households in the poor wealth category, the remittance receiving
household finances increased and the food security status improved. However, when compared to
wealthier households, receiving of remittances did not affect the household food security. A systematic
review by Thow et al. [18] focused on the impacts of remittances on diets and nutrition, and the studies
revealed that households with remittances had better food consumption, minimized vulnerability,
and better food security than the households that did not have remittances. Using the ordered logistic
regression, Atuoye et al. [19] investigated the impacts of remittances on household food security among
rural and urban households in Ghana. The findings demonstrated that rural and urban households
that received remittance were more likely to be in the severe food insecurity category than urban
households without remittances. Bhalla et al. [20] studied the impacts of cash transfers on household
food security in Zimbabwe and revealed that that cash transfer is a major determinant of household
food security and diet diversity. The results further demonstrated an improvement in food security for
households that are recipients of cash transfers.
 Livestock incomes and ownership are viewed as a potential approach to help minimize food
security. Dumas et al. [21] investigated the impact of livestock ownership on food consumption in eastern
Zambia. The results did not show association between livestock ownership and dietary diversity among
the children in Zambia. Using the food security index, Demeke et al. [22] examined the determinants
of household food security in rural Ethiopia and found that households with more livestock ownership
were less likely to be food-insecure. Mango et al. [23] applied linear regression to evaluate the factors
that affect food security among smallholder farmers in Zimbabwe and found that livestock ownership
and income contributed to better household food security. Rawlins et al. [24] indicated the importance
of livestock in improving nutritional status of rural households in Rwanda. In a study in sub-Saharan
Africa, Hetherington et al. [25] demonstrated that livestock ownership is associated with increased
food consumption.
 Agricultural land ownership is another factor identified in previous studies as associated with food
security. Robertson and Pinstrup-Andersen [26], for example, argued that food security is threatened as
the majority of smallholder farmers lack formal users’ rights to agricultural land in developing countries.
Similarly, Headey and Jayne [27] noted that issues of land constraints are of relevance in Africa, and the
land tenure systems are part of that concern to ensure food security [28]. In addition, Holden and
Ghebru [5] recognized that enhanced agriculture and productivity eventually lead to improved food
security. A study that Chirwa [29] conducted in Malawi focused on land tenure systems and food
production, and the results indicated that households that benefited from land reform programs to
strengthen their land tenure security from customary land reported high maize production and an
increase in food security when compared to non-beneficiaries in customary land. Simbizi et al. [30] e
investigated the role that tenure security plays in rural, poor sub-Saharan Africa, and their findings
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indicated that land security is a major determinant of food security. Mwesigye et al. [31] indicated that
private ownership yielded higher crop outputs when compared to customary land tenure systems in
Uganda. A private owner was considered to have secure land rights, compared to the customary land
tenure with limited land use rights. Michler and Shively [32] studied the relationship between land
tenure and efficiency in farm productivity in the Philippines, and their findings indicated that land
tenure contributed to farm productivity. Moreover, research by Mendola and Simtowe [33] in Malawi
indicated that access to a secure productive resource such as land enhances food security. A study
by Santos et al. [34] analyzed the land allocation and registration program in India’s West Bengal to
evaluate whether government-allocated land contributed to food security. Their findings revealed that
no statistical association was observed to impact food security from government land. For Zambia, less
research was carried out to explore the land tenure systems [35]. The study by Smith [36] in Zambia
demonstrated that formal land titles enhanced investment and were more profitable, with higher
output on agricultural productivity. Merten and Haller [37] studied the role of property rights on child
growth and the food security of households in customary land tenure in Zambia, and they found that
insecure property rights in the form of type of land tenure affected the food consumption pattern of the
households. Sitko et al. [38] analyzed the effects of land titling among smallholder farmers with the
objective of determining whether it enhanced growth in agriculture. Their results did not demonstrate
any statistical differences between title and non-title holders.
 With regard to the influence of farm size on welfare outcomes, Khonje et al. [39] studied the
effect of adopting improved maize varieties on welfare outcome indicators, namely food security,
poverty, crop income, and consumption expenditure, in the eastern province of Zambia. The findings
on farm size and poverty revealed an inverse relationship. Households with a smaller farm size
of 0.1–3.5 hectares showed higher poverty levels in 54% cases as compared to households with
more than 3.5 hectares, where the poverty levels were in 33% cases. Frelat et al. [40] showed that
farm size is a determinant of food security in sub-Saharan Africa. The relationship showed that,
as farm size increases, the probability of a household being food-secure also increases. A study by
Koirala et al. [41] investigated the role of land ownership on productivity among rice farmers in the
Philippines and found that a 1% increase in farm size increased the rice yield by 0.40%. Paul and
Wa Githinji [42] examined the relationship between farm size and productivity in Ethiopia. The results
demonstrated an inverse negative association between farm size and per hectare yield.
 Agricultural group membership is viewed as a vital institution and pathway for smallholder
farmers to participate in markets, raise incomes, and eventually reduce poverty. The group membership
can for example provide networking and connections which may empower individuals or groups
with various business ventures to enhance income generation, and nutritional programs to address
issues of food insecurity. Fischer and Qaim [43] investigated the role of agricultural cooperatives
among smallholder banana farmers in Kenya. They found that members of farmer groups marketed
their produce collectively, yielded a higher price, and had higher income than non-members who
marketed individually. Verhofstadt and Maetens [44] analyzed the benefits of farmer membership in a
cooperative on poverty in Rwanda. The results of the propensity matching score demonstrated that
farmers that belonged to cooperatives had better income and reduced levels of household poverty.
Furthermore, Verhofstadt and Maetens [44] argued in support of agricultural group as they are
related to collective participation and more inclusive than other innovations that focus on individuals.
Abate et al. [45] focused on the impacts of agricultural cooperatives on enhancing the efficiency of
smallholder farmers in Ethiopia. The findings showed that agricultural cooperatives contributed to
higher farm productivity. Ma and Abdulai [46] analyzed the effect of cooperative membership on
household welfare of apple farmers in China and found members of farmers groups to have better
farm yields and household income. Mojo et al. [47] evaluated the determinants and economic benefits
of membership in coffee cooperatives in rural Ethiopia. The results indicated that membership to a
cooperative positively contributed to household incomes.
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 Concerning access to credit, Aidoo et al. [48] investigated the determinants of household food
security in rural Ghana. The results of the logistic regression model analysis revealed that access to
credit had a positive influence on a household’s food security. In Nigeria, Awotide et al. [49] examined
the effect of access to credit on agricultural productivity among smallholder farmers and demonstrated
that households with access to credit had higher cassava productivity. In support of alleviating the
constraints related to credit access for smallholder farmers, Tirivayi et al. [50] argued for agricultural
interventions of establishing microcredit and microfinance institutions in rural areas.
 Given the lack of consensus on indicators to measure food security, Carletto et al. [51] suggested
that a useful approach is to assess the food security situation of each dimension and specify the level
(national, regional, or household). In addition, the research of Headey and Ecker [52], in agreement,
revealed that, in measuring food security, a criterion to gauge the indicators is based on the demand of
decision-makers for a wide range of information. Vaitla et al. [53] argued that, rather than focusing on
one indicator, the best way to capture the food security measurement is to see the complementarity.
For this reason, considering one indicator alone cannot necessarily reflect the food situation. Therefore,
two indicators were incorporated to measure food security, namely the food consumption score (FCS)
and the household hunger scale (HHS).

3. Data and Methodology

3.1. Study Area
 The study area comprised four districts, namely Choma, Mazabuka, Kalomo, and Chikankata,
in the southern province of Zambia (Figure 1). In Zambia, agriculture employs 72% of the country’s
labor force, with more than 60% residing in rural areas [54]. The predominant livelihood activity is
smallholder farming, mainly cultivating maize and livestock raring. The study area is classified under
moderate rainfall patterns characterized with approximately 800–1000 mm of annual precipitation.
The soils in the region are characterized as sand loamy and clay loams. The farming system integrates
crop production and livestock rearing as a mixed type of farming. Smallholder crop production
includes cereals, tubers, and legumes. Cash crops such as sunflower, cotton, tobacco, and soya beans
are also cultivated. They also rear livestock, mainly cattle, goats, and poultry. The communal lands are
open for livestock grazing usually after crop harvests, while, at the same time, the land tenure rights are
respected. Regarding cultural characteristics, the study area is home to the Tonga people who are the
main ethnic group. In Tonga culture, the number of cattle owned defines the social status. Households
keep and sell goats, pigs, and poultry to be able to pay for immediate needs such as health bills
and education. In the survey area, the farmers learned the different aspects of agriculture mainly by
sharing their knowledge through networking in farming groups and/or information dissemination by
extension services. Agriculture extension support is further coordinated by the Ministry of Agriculture
and cooperatives through extension workers. It provides agriculture-related information on the
television and radio, and organizes agriculture shows at the district, provincial, and national level.
Agriculture extension is important as it helps farmers decide on whether to choose new technologies
and increase production.
 In Zambia, agricultural land ownership is categorized into two regimes, namely (i) the customary
land tenure that accounts for 60%, and (ii) the statutory land tenure that accounts for 40%. In customary
tenure, land is controlled by the traditional leaders in the communities. This land is informally
recognized, and it lacks tenure security that results in it having limited land users’ rights, which increases
the chances of farmer eviction from the land. In contrast to customary tenure, a statutory land tenure
is issued with land titles that indicates exclusive ownership, full land rights, and protection from
eviction [55]. Zambia has a total land mass of 752,621 km2 [56]. Despite the abundance of land,
the possibility of agricultural growth is increasingly challenging due to smallholder farmers’ limitations
to land access [57]. The policies on land in Zambia remained stagnant for decades, as the policy-makers
often do not consider the smallholder farmers’ land constraints [58]. The national development plan
Sustainability 2019, 11, 2715 6 of 18



report for 2017–2021 indicated that there is low access to land in Zambia, despite it being a vital
resource for investment, the creation of wealth, and ultimately contributing to poverty reduction [59].
To reduce the challenges that smallholder farmers face in accessing land, strategies for assessments of
land distribution and governance play an important role [60].
Sustainability 2019, 11, x FOR PEER REVIEW 6 of 17




 Figure 1. Study site.

3.2. Data Collection
3.2. Data Collection and
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study was based on face-to-face interviews with smallholder farmers using a structured using a structured questionnaire.
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 district commissioner’s office.

3.3. Research Variables

3.3.1. Food Security Indicators
 The FCS was developed by the World Food Program as a frequency-weighted dietary diversity
score [62]. Different studies applied the FCS indicator in Tanzania [63], Rwanda [64], and Kenya [65].
 The FCS is calculated as follows [66]:
 FCS = _1 _1 + _2 _2+. . . _8 _8, (1)
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3.3. Research Variables

3.3.1. Food Security Indicators
 The FCS was developed by the World Food Program as a frequency-weighted dietary diversity
score [62]. Different studies applied the FCS indicator in Tanzania [63], Rwanda [64], and Kenya [65].
 The FCS is calculated as follows [66]:

 FCS = a_1 b_1 + a_2 b_2 + . . . a_8 b_8, (1)

where a = frequency (one-week recall period), 1−8 = food group, and b = weight (meat, milk,
and fish = 4; pulses = 3; staples = 2; vegetables and fruits = 1; and oil and sugar = 0.5).
 The threshold for the FCS classifies households into one of the following categories: poor (35).
 The HHS was developed by the Food and Nutrition Technical Assistance. It is a cross-culturally
validated food security indicator that captures elements of cultural experiences and severe food
insecurity, and it was applied across studies conducted in Kenya, Zimbabwe, South Africa, Mozambique,
Malawi, and the Gaza Strip [66,67]. A four-week recall period is set as standard in data collection.
The HHS questionnaire consists of the following three questions: (i) Was there ever no food at all in
your household because there were no resources to get more? (ii) Did you or any household member
go to sleep at night hungry because there was not enough food? (iii) Did you or any household
member go a whole day and night without eating anything because there was not enough food?
The responses to the questions were classified as follows: rare = 0 (twice a month), sometimes = 1 (three
to 10 times), and often = 2 (>10 times). The values were added up for the three questions, and the
scores ranged from 0–6. The HHS categories are as follows: little to no hunger (scores 0–1), moderate
hunger (scores 2–3), and severe hunger (scores 4–6) [62].

3.3.2. Ordered Probit Model
 The ordered probit regression model was used to examine the effect of the chosen factors as
influencers on food security.

Ordered Probit Model
 The dependent variables are categorical and ordinal; therefore, the ordered probit regression
model is more suitable for the analysis than multinomial regression or a probit regression model [68].
 The ordered probit model regression is calculated with the following equation:

 y_iˆ∗ = xi β + ε_i, (2)

where y∗i is an unobserved random variable, x is a vector of socioeconomic variables assuming normal
distribution, εi = N (0, 1), and i = 1, 2, . . . , N.
yi is the observable ordinal variable, yi = j if µ j−1 < y∗i ≤ µ j ,,
where j = 0, 1, . . . , n, µ−1 = −∞, and µn = +∞.
 The probability is calculated with the following interval decision rule:
    
 Prob [ yi = j] = Φ µ j − xi β − Φ µ j−1 − xi β , (3)

where Φ denotes the cumulative distribution function, and j represents the categories of
dependent variables.
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Dependent Variables
 The dependent variables were the FCS and the HHS food security indicators. The FCS indicator is
ordered into three categories, namely poor, borderline, and acceptable. The HHS is also classified into
three categories: severe hunger, moderate hunger, and little to no hunger.

Explanatory Variables
 The selection of explanatory variables was based on findings of previous research. The variables
were classified into four groups: (i) household head characteristics, (ii) household characteristics,
(iii) farm characteristics, and (iv) institutional characteristics. The household head variables included
gender, age, education level, marital status, and farming experience; household characteristics included
household size, self-employment, remittances, and off-farm and livestock income; farm characteristics
included land ownership and land size; and institutional characteristics included access to credit and
membership to farmer groups. The variables were tested for multicollinearity. The variance inflation
factor (VIF) values were in the range lower than 10, indicating no multicollinearity problems.
 The Statistical Package for Social Sciences IBM (SPSS) and STATA software were used for the
data analysis.

4. Results

4.1. Description of Model Variables
 The model variables used in this study are presented in Table 1. The mean FCS value was 27,
while the HHS had a mean value of 1.5. In relation to gender of household heads, the majority of the
households were led by men, accounting for 63%. The level of education demonstrated that 22.3% of
the household heads did not have any form of education, while 37% and 39% were indicated as having
primary and secondary education, respectively. The average size of the household was made up of
seven members. In this study, the non-farm incomes were divided as self-employment activities that
included business activities such as shop-keeping, charcoal sales, and hand crafts, while the off-farm
activities included formal and informal non-agricultural wages. In this category, 42.7% of households
indicated off-farm income and 26% of the households received remittances. Livestock ownership is an
important asset in the southern province. As many as 61.5% of the households indicated livestock
ownership, ranging from poultry to pigs, goats, and cattle. Some sold the livestock to boost their
subsistence income. The average livestock income was 1087 Zambian kwacha. Agricultural land
ownership was categorized as statutory and customary tenure system. The average land size of
smallholder farmers was 3.2 hectares; however, the majority were categorized under less than a hectare.
Access to credit was constrained, and this could be attributed to poor establishment of financial
institutions targeting smallholder farmers. More than 50% of the smallholder farmers were indicated
as participating in farmers groups.

 Table 1. Description of variables.

 Mean
 Variable Description
 (n = 400)
 Food security indicators
 Three categories: poor (35) (19.91)
 Three categories: little to no hunger (0–1), moderate 1.52
 Household hunger scale (HHS)
 hunger (2–3), severe hunger (4–6) (0.74)
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 Table 1. Cont.

 Mean
 Variable Description
 (n = 400)
 Household head characteristics
 Gender Sex of household head (male = 1) 63.0%
 Age Number of years for household head 40.81 (13.30)
 Education level 0 = none, 1 = primary, 2 = secondary, 3 = tertiary 0 = 22.3%
 Farming experience Number of years spent in farming 10.00 (9.79)
 Marital status Married = 1 85.8%
 Household characteristics
 Household size Number of members 6.70 (3.26)
 Self-employment Household has business (yes = 1) 51.5%
 Remittances Family received money from relatives (yes = 1) 26.30%
 Off farm Household has salaried or waged incomes (yes = 1) 42.70%
 Household has an income from livestock sales
 Livestock income 1087.34 (2947.59)
 (Zambian kwacha)
 Farm characteristics
 Land ownership 1 = statutory, 2 = customary n = 400
 Land size Size of agricultural land in hectares 3.26 (2.82)
 Institutional characteristics
 Access to credit Household has access to credit (yes = 1) 16.30%
 Member of farming group Household belongs to farming group (yes = 1) 51.50%
 Note: The mean values are reported with the standard deviation in parentheses. Percentages are reported as
 indicated; $1 United States dollar (USD) = 10 Zambian kwacha.


4.2. Influencers on Food Security
 The results of the ordered probit models of the factors affecting food security are presented in
Tables 2 and 3.

 Table 2. Ordered probit regression model (FCS).

 Variables Coefficient Food Consumption Score
 Poor Borderline Acceptable
 Household head characteristics
 0.098 −0.026 0.013 0.026
 Gender
 (0.146) (0.039) (0.020) (0.038)
 −0.011 0.003 −0.002 −0.003
 Age
 (0.007) (0.002) (0.001) (0.002)
 0.472 *** −0.126 *** 0.061 *** 0.126 ***
 Education level
 (0.092) (0.023) (0.016) (0.025)
 −0.012 0.003 −0.002 −0.003
 Farming experience
 (0.010) (0.003) (0.001) (0.003)
 −0.288 * 0.077 * −0.037 * −0.077 *
 Marital status
 (0.161) (0.043) (0.023) (0.042)
 Household characteristics
 0.084 *** −0.023 *** 0.011 *** 0.022 ***
 Household size
 (0.025) (0.006) (0.004) (0.007)
 0.021 −0.006 0.003 0.006
 Self-employment
 (0.138) (0.037) (0.018) (0.037)
 −0.036 −0.064 *
 Remittances −0.256(0.157) 0.068(0.042)
 (0.024) (0.038)
 −0.388 *** 0.104 *** −0.050 *** −0.104 ***
 Off farm
 (0.085) (0.021) (0.014) (0.023)
 0.000 *** −0.000 *** 0.000 *** 0.000 ***
 Livestock income
 (0.000) (0.000) (0.000) (0.000)
Sustainability 2019, 11, 2715 10 of 18



 Table 2. Cont.

 Variables Coefficient Food Consumption Score
 Poor Borderline Acceptable
 Farm characteristics
 −0.485 *** 0.129 *** −0.061 ** −0.129 ***
 Land ownership
 (0.137) (0.035) (0.020) (0.037)
 0.091 *** −0.024 *** 0.012 *** 0.024 ***
 Land size
 (0.026) (0.007) (0.004) (0.007)
 Institutional characteristics
 0.128 −0.034 0.016 0.036
 Access to credits
 (0.185) (0.049) (0.021) (0.053)
 0.301 ** −0.081 ** 0.039 ** 0.079 **
 Farming group member
 (0.143) (0.038) (0.019) (0.038)
 0.431
 Cut1
 (0.363)
 1.202
 Cut2
 (0.367)
 Number of observations 400
 Prob > chi2 0.000
 Pseudo R2 0.264
 Note: *** p < 0.01, ** p < 0.05, * p < 0.1. The average marginal effects are reported with the standard errors
 in parentheses.


 Table 3. Ordered probit regression model (HHS).

 Variables Coefficient Household Hunger Scale
 Severe Hunger Moderate Hunger Little to No Hunger
 Household head characteristics
 0.009 0.001 0.002 −0.002
 Gender
 (0.147) (0.014) (0.034) (0.036)
 0.007 0.001 0.002 −0.002
 Age
 (0.006) (0.001) (0.002) (0.002)
 −0.468 *** −0.044 *** −0.120 *** 0.116 ***
 Education level
 (0.096) (0.012) (0.025) (0.022)
 −0.003 −0.000 −0.001 0.001
 Farming experience
 (0.010) (0.001) (0.002) (0.002)
 0.010 0.001 0.002 −0.002
 Marital status
 (0.103) (0.010) (0.024) (0.026)
 Household characteristics
 0.000 0.000 0.000 −0.000
 Household size
 (0.027) (0.003) (0.006) (0.007)
 −0.101 −0.010 −0.024 0.025
 Self-employment
 (0.142) (0.013) (0.033) (0.035)
 0.015 0.001 0.004 -0.004
 Remittances
 (0.165) (0.016) (0.038) (0.041)
 0.201 ** 0.019 ** 0.047 ** -0.050 **
 Off farm
 (0.792) (0.008) (0.019) (0.019)
 −0.000 *** −0.000 *** −0.000 *** 0.000 ***
 Livestock income
 (0.000) (0.000) (0.000) (0.000)
 Farm characteristics
 0.354 ** 0.033 ** 0.086 ** −0.088 **
 Land ownership
 (0.145) (0.015) (0.034) (0.035)
 −0.215 *** −0.020 *** −0.050 *** 0.053 ***
 Land size
 (0.044) (0.005) (0.011) (0.010)
Sustainability 2019, 11, 2715 11 of 18



 Table 3. Cont.

 Variables Coefficient Household Hunger Scale
 Severe Hunger Moderate Hunger Little to No Hunger
 Institutional characteristics
 −0.077 −0.007 −0.018 0.019
 Access to credits
 (0.219) (0.019) (0.050) (0.054)
 Farming group −0.706 *** −0.069 *** −0.159 *** 0.175 ***
 member (0.155) (0.019) (0.036) (0.036)
 −0.6856
 Cut1
 (0.356)
 0.3834
 Cut 2
 (0.358)
 Number of observations 400
 Prob > chi2 0.000
 Pseudo R2 0.270
 Note: *** p < 0.01, ** p < 0.05, * p < 0.1. The average marginal effects are reported with the standard errors
 in parentheses.


5. Discussion

5.1. Household Head Characteristics
 In the FCS model, household heads who were more educated were 12.6% less likely to be in
the poor FCS category, 6.1% were more likely to be borderline, and 12.5% were more likely to be in
the acceptable category of FCS than their less educated counterparts. Regarding the HHS model,
our findings indicate that, with an increase in education level, there was a respective 4.4% and 12% lower
probability of households being in the severe hunger and moderate hunger categories, while 11.6%
had more chance of being in the little to no hunger category. This result is similar to the work of
Mason et al. [63], who used the food consumption as an indicator of food security to determine the
factors influencing food security in Tanzania. They found that households featuring a household head
with a higher education level had better food security status.

5.2. Household Characteristics
 The households that had off-farm income were 10% more likely to be in the poor FCS category
in this study, while 5% were less likely to be in the borderline FCS category and 10% were less likely
to be in the acceptable FCS category than those who did not. The HHS indicated that an increase in
off-farm activities resulted in the likelihood of a household to be in a severe hunger category being 1.9%,
while 4.7% of households were more likely to be in the moderate hunger category, and 5.3% were less
likely to be in the little to no hunger category. This finding can be attributed to the fact that households
devoted more time to off-farm activities at the expense of farm activities so that they may provide
higher food production for their own consumption. With similar results, Mabuza et al. [69] analyzed
the impact of income sources on household food insecurity in Swaziland. Their findings reported that
on-farm income-dependent households were more food-secure when compared to their counterparts
that depended on off-farm income sources. Beyene and Muche [70], in Ethiopia, indicated that off-farm
incomes positively contributed to the household food security. The policy aspect would seek how
to develop formal employment opportunities that would enhance income levels of the household.
The improvement in conditions services would increase the number of people able to acquire food and
improve their food security status to substantiate the farm incomes.
 According to our results, an increase in livestock incomes was associated with a lower likelihood
of being in the poor FCS category, and a higher likelihood of being in the borderline and acceptable
FCS categories. Similarly, the HHS demonstrated that an additional increase in livestock income
reduced the probability of the household of being in the severe and moderate hunger categories,
while it increased the probability of being in the little to no hunger category. The explanation to
this result is that ownership of livestock potentially provides meat, milk, and other quality dairy
Sustainability 2019, 11, 2715 12 of 18



products, and increases the quantity of nutritional foods for the households. Secondly, livestock
sales usually involving live animals enhance income, which may improve the purchasing power
of the household. In support of the importance of livestock ownership and incomes to improving
food security, Jodlowski et al. [71], who studied the impact of livestock on food security in Zambia,
demonstrated that livestock ownership and sales contributed to the household food security through
an increase in food consumption expenditure and dietary diversity. Similarly, Kafle et al. [72] studied
the role of livestock transfer programs among poor secure households in Zambia. Their result revealed
an increase in the financial capacity and household food security status, which was enhanced by
training of households in livestock management topics. In contrast to our finding, Silvia et al. [73],
who analyzed the determinants of farm household food security in Kenya, Uganda, and Tanzania,
found that ownership of livestock did not contribute to the enhancement of household food security.

5.3. Farm Characteristics
 Regarding land tenure as a determinant of FCS (Table 2), the findings indicated that the households
with customary land tenure were 12.9% more likely to be in the poor FCS category, while 6% were
less likely to be in the borderline FCS category, and 12.9% were less likely to be in the acceptable
FCS category than households with statutory land tenure. Similarly, the effect on land tenure as
a determinant of HHS (Table 3) revealed that households with customary land tenure were 3.3%
more likely to be in the severe hunger category, while 8.1% were more likely to be in the moderate
hunger category, and 8.7% were less likely to be in the little to no hunger category when compared to
households under the statutory land tenure. A study in Bangladesh by Nasrin and Uddin [74] analyzed
tenure systems that were classified as share tenants without land rights and cash tenants who held
secure land rights. The study found higher food security in households that had secure land rights.
Our results are in line with Ghebru and Holden [75], who demonstrated that tenure secure households,
measured by the provision of land certificates, had a positive association with food security in Ethiopia.
Furthermore, our findings complement those found by Mueller et al. [76] who studied the benefits of
land reform programs for households, providing them with land titles to strengthen their land property
rights on food security in Malawi. They demonstrated that food security of the households with more
secure property rights improved in the long term. Apart from land property rights, an increase of land
size in resettlement schemes also contributed to food security.
 The results of our model showed that a one-hectare increase in land size is associated with being
2.4% less likely to be in the poor FCS status, 1.1% more likely to be in the borderline status, and 2.4%
more likely to be in the acceptable FCS status. Similarly, the HHS model demonstrated that the
probability of a household with one-hectare larger land size was reduced by 2% and 5% with regard to
being in the severe hunger category and moderate hunger category, respectively while the probability
of being in the little to no hunger category increased by 5.3%. One plausible explanation is that
agriculture households with larger land size may have crop diversity, providing more nutritious crops
when compared to households with smaller land size, who may highly consider cultivating only staple
cereals. Githinji [77] studied how land influences household poverty levels in Kenya. The findings
showed that an increase in land size reduced the probability of households being in the poor poverty
levels. Furthermore, our finding is in agreement with that of Rammohan and Pritchard [78], who used
ordered probit models to estimate if land holding was a determinant of household food and nutrition
security in Myanmar. Their result indicated that an increase in land size enhanced household food
security status. Similarly, our result is in alignment with that of Muraoka et al. [79], who analyzed the
relationship between land access and food security in Kenya. They demonstrated that an increase in
land size resulted in a rise in household food security.

5.4. Institutional Characteristics
 The households that are members of a farming group or cooperative were indicated as being 8%
less likely to be in the poor FCS category, while they had respectively 3.9% and 7.9% more chance of
Sustainability 2019, 11, 2715 13 of 18



being in the borderline and acceptable FCS categories than those who were not. The HHS revealed
that membership to a farmer’s organization reduced the probability of a household being in the severe
hunger category by 6.9%, while such a household was 15.9% less likely to be in the moderate hunger and
17.5% more likely to be in the little to no hunger category. The results are in line with Nugusse et al. [80],
who examined the association of cooperative and food security in northern Ethiopia. The study revealed
that 21% of households with cooperative membership were food-insecure, while 35% of households
without cooperative membership were food-insecure. Similarly, Wossen et al. [81] studied the effects
of access to extension services and membership to cooperatives on household welfare in rural Nigeria.
The results indicated that extension access and cooperative membership had a positive relationship
with poverty reduction.

6. Conclusions
 The main aim of this study was to examine the association of the chosen socioeconomic factors as
influencers on food security. The study was conducted in 2016 in the southern province of Zambia.
Food security was measured by the food consumption score (FCS) and the household hunger scale
(HHS) indicators. From our sample, both the FCS and HHS ordered probit models’ findings revealed
that higher education levels of household head, increasing livestock incomes, secure land tenure,
increasing land size, and group membership increased the probability of household food security.
 Considering the fact that a larger number of households keep livestock based on cultural tradition,
strengthening of livestock ownership and incomes should be prioritized. Support toward training and
animal husbandly development with respect to environmental challenges and animal diseases may
enhance the livestock production. According to the results of this study, we can expect that livestock
development programs such as training of farmers in animal husbandry would improve livestock
productivity and, thus, increase food security.
 To improve household food and nutritional security in the long run, the development of food
and land policies that are in accordance with the revealed determinants of food security may be
recommended. The results of our study show that land tenure security increases food security. Thus,
to increase food security, measures that would safeguard higher land security for households under
customary tenure should be introduced. The most important in this respect is the implementation
of a more effective land rights protection law. To speed up the process, stakeholders such as the
national farmers union or local municipalities need to lobby the central government to implement a
more effective law. Increased tenure security could be achieved, for example, through the inclusion of
customary tenured households in land registration programs with legal recognition.
 The size of land was found to have a positive relationship with food security. Therefore, pursuit
of policies that help smallholder farmers with holdings of arable land, especially in customary land
tenure, must be promoted. Recently, risks of some local traditional authorities not collaborating with
communities within their authority in some instances gave rise to land grabbing. This is a case where
the traditional leaders (chiefs) can decide to rent part of the land to an enterprise and make the land
size of domestic farmers smaller. The decreasing farm size may affect the agricultural productivity of
smallholder farms and limit their potential of attaining better food security.
 Our findings demonstrate a positive impact of farming group membership on food security.
Therefore, interventions to support organization and empowerment of existing informal and formal
groups, especially through community mobilizing, should be encouraged by private and government
organizations. Facilitation of official registration of farmers groups at agricultural district offices should
be a priority. The registration must be planned beyond the current situation, where the majority of
groups are only organized and oriented toward benefiting from programs such as the farmers input
support. Only registered farming groups may provide training of members to help them improve
the household food security status. Furthermore, farmers groups create opportunities for sharing of
experiences among farmers and with other existing groups. Empowerment of farmers groups through
adequate policy measures has the potential to improve household food security.
Sustainability 2019, 11, 2715 14 of 18



 With regard to future research on farmers’ education, from the methodological point of view,
it would be interesting to include more variables representing knowledge acquisition other than the
level of household head education in the survey and analysis, to help understand in more depth
how receiving information helps decision-making toward food and nutritional security. Concerning
land ownership studies, a focus on perceived tenure insecurity and inequalities among women and
youth who are mostly reported as marginalized in traditional land with respect to land holding and
agricultural output may be of interest for consideration. Regarding membership to farmer groups,
an area of potential further research may focus on factors and barriers that motivate farmers to
participate or not to participate in cooperatives. This study was limited by regional coverage within
Zambia; however, the findings provide a fundamental base regarding the determinants of food security.

Author Contributions: W.N., methodology, investigation, writing—original draft preparation; M.B.,
methododology, data curation, formal analysis, writing—review and editing; J.B., conceptualization, methodology,
writing—review and editing, supervision, project administration, funding acquisition.
Funding: This research was funded by Internal Grant Agency of Faculty of Tropical AgriSciences: grant number
20195006, and grant number 20195008.
Conflicts of Interest: The authors declare no conflict of interest.

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